mirror of
https://github.com/MLSysBook/TinyTorch.git
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753ae52ae0
This commit implements the pedagogically optimal "inevitable discovery" module progression based on expert validation and educational design principles. ## Module Reordering Summary **Previous Order (Problems)**: - 05_losses → 06_autograd → 07_dataloader → 08_optimizers → 09_spatial → 10_training - Issues: Autograd before optimizers, DataLoader before training, scattered dependencies **New Order (Beautiful Progression)**: - 05_losses → 06_optimizers → 07_autograd → 08_training → 09_spatial → 10_dataloader - Benefits: Each module creates inevitable need for the next ## Pedagogical Flow Achieved **05_losses** → "Need systematic weight updates" → **06_optimizers** **06_optimizers** → "Need automatic gradients" → **07_autograd** **07_autograd** → "Need systematic training" → **08_training** **08_training** → "MLPs hit limits on images" → **09_spatial** **09_spatial** → "Training is too slow" → **10_dataloader** ## Technical Changes ### Module Directory Renaming - `06_autograd` → `07_autograd` - `07_dataloader` → `10_dataloader` - `08_optimizers` → `06_optimizers` - `10_training` → `08_training` - `09_spatial` → `09_spatial` (no change) ### System Integration Updates - **MODULE_TO_CHECKPOINT mapping**: Updated in tito/commands/export.py - **Test directories**: Renamed module_XX directories to match new numbers - **Documentation**: Updated all references in MD files and agent configurations - **CLI integration**: Updated next-steps suggestions for proper flow ### Agent Configuration Updates - **Quality Assurance**: Updated module audit status with new numbers - **Module Developer**: Updated work tracking with new sequence - **Documentation**: Updated MASTER_PLAN_OF_RECORD.md with beautiful progression ## Educational Benefits 1. **Inevitable Discovery**: Each module naturally leads to the next 2. **Cognitive Load**: Concepts introduced exactly when needed 3. **Motivation**: Students understand WHY each tool is necessary 4. **Synthesis**: Everything flows toward complete ML systems understanding 5. **Professional Alignment**: Matches real ML engineering workflows ## Quality Assurance - ✅ All CLI commands still function - ✅ Checkpoint system mappings updated - ✅ Documentation consistency maintained - ✅ Test directory structure aligned - ✅ Agent configurations synchronized **Impact**: This reordering transforms TinyTorch from a collection of modules into a coherent educational journey where each step naturally motivates the next, creating optimal conditions for deep learning systems understanding.
39 lines
1.1 KiB
TOML
39 lines
1.1 KiB
TOML
[build-system]
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requires = ["setuptools>=64.0"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "tinytorch"
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version = "0.0.1"
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description = "🚧 TinyTorch: Educational Deep Learning Framework (Coming Soon)"
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readme = "README_placeholder.md"
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requires-python = ">=3.8"
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authors = [
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{name = "Vijay Janapa Reddi", email = "vj@eecs.harvard.edu"}
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]
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license = "MIT"
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classifiers = [
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"Development Status :: 2 - Pre-Alpha",
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"Intended Audience :: Education",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.8",
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"Programming Language :: Python :: 3.9",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.11",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Topic :: Education",
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]
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dependencies = []
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[project.urls]
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Homepage = "https://github.com/VJ/TinyTorch"
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Repository = "https://github.com/VJ/TinyTorch"
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Issues = "https://github.com/VJ/TinyTorch/issues"
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[tool.setuptools.packages.find]
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where = ["."]
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include = ["tinytorch_placeholder*"]
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[tool.setuptools.package-dir]
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tinytorch = "tinytorch_placeholder"
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